Project Summary Agent-based modeling is a powerful computational technique for simulating the spatiotemporal dynamics of multiple players in a virtual environment, revealing emergent behaviors that would otherwise not be readily apparent. Here, we propose to use agent-based modeling to gain an improved understanding of the cellular events involved in lung inflammation and development. First, we posit that the control of the normal allergic inflammatory response needs to be better understood both in terms of how it is turned on and how it is turned off. We have recently used agent-based modeling to test what we call the Inflammatory Twitch Hypothesis of allergic inflammation in the lung, which holds that allergic inflammation is controlled in a manner that is formally similar to the control of skeletal muscle activation. The unit of activity in the case of inflammation is the inflammatory twitch, which is a sequence of self-resolving events that are repetitively invoked only so long as antigen is present. Our agent-based computational model of the allergic inflammatory response has shown that continual stimulation by antigen results in cycles of tissue damage and repair interspersed with periods of non- responsiveness resembling a refractory period. These findings are consistent with the inflammatory twitch hypothesis and suggest that the allergic inflammatory response is controlled via frequency modulation. We now propose to refine our agent-based model and to perform experiments with allergen sensitized and challenged mice determine the length of the refractory period, enabling a better understanding of the timescale of the allergic inflammatory twitch. In addition, we will employ our agent-based modeling techniques to simulate the engraftment, movement, proliferation and differentiation of mesenchymal stem cells inoculated into decellularized lung scaffolds. The simulations will be compared to experimental data collected by colleagues at the Vermont Lung Center, thereby providing a theoretical/computational framework upon which to interpret the experimental results and inform the design of further experiments. We have already designed prototype agent- based models of stem cell population dynamics in 2D and 3D environments, and will refine these models with the aim of predicting how the spatial distribution of integrin binding sites and chemical environment determined patterns of stem cell engraftment as a function of time.